Publication Type

Journal Article

Version

publishedVersion

Publication Date

11-2016

Abstract

We propose a way to estimate a generalized recursive route choice model. The model generalizes other existing recursive models in the literature, i.e., (Fosgerau et al., 2013b; Mai et al., 2015c), while being more flexible since it allows the choice at each stage to be any member of the network multivariate extreme value (network MEV) model (Daly and Bierlaire, 2006). The estimation of the generalized model requires defining a contraction mapping and performing contraction iterations to solve the Bellman’s equation. Given the fact that the contraction mapping is defined based on the choice probability generating functions (CPGF) (Fosgerau et al., 2013b) generated by the network MEV models, and these CPGFs are complicated, the generalized model becomes difficult to estimate. We deal with this challenge by proposing a novel method where the network of correlation structures and the structure parameters given by the network MEV models are integrated into the transport network. The approach allows to simplify the contraction mapping and to make the estimation practical on real data.We apply the new method on real data by proposing a recursive cross-nested logit (RCNL) model, a member of the generalized model, where the choice model at each stage is a cross-nested logit. We report estimation results and a prediction study based on a real network. The results show that the RCNL model performs significantly better than the other recursive models in fit and prediction.

Keywords

Recursive network MEV, Contraction mapping, Integrated network, Recursive cross-nested, Value iteration, Maximum likelihood estimation, Cross-validation

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

Transportation Research Part B: Methodological

Volume

93

First Page

146

Last Page

161

ISSN

0191-2615

Identifier

10.1016/j.trb.2016.07.016

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.trb.2016.07.016

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